1.1 Life Expectancy Summary for 2022 - By Race and Ethnicity and Sex

• Life expectancy is a key summary measure of disparity, and succinctly summarizes differences in mortality between groups.
  
  This chart shows Life Expectancy at Birth in 2022 for males and females, for each race and ethnic group. Calculation of life expectancy is complex, and requires use of assumptions applied to high-quality data.

• For all race and ethnic groups, females live longer than males. Among males, the life expectancy for Black males is 12.0 years less than for Asian males; among females the difference is 10.1 years. This difference is caused by a cascade of inequities in social determinants of health, and other systemic factors.

• The red slashes at the bottom of the y-axis indicate that the scale of the y-axis is discontinuous. The y-axis does not start at 0, but rather at age 65, so that the important differences in life expectancy can be seen clearly.

1.2 Life Expectancy by Race and Ethnicity and Sex, by Year, 2000 to 2022

• This chart shows the trends in Life Expectancy at Birth for the past 23 years by sex and race and ethnicity.

• Life expectancy steadily increased for all groups over this period, until 2020. In 2020 there was a sharp drop for all groups due to the impact of COVID-19. From 2020 to 2021, life expectancy continued to decrease, albeit less sharply, for males of all race and ethnic groups, and for Latina females. Life expectancy increased in 2021 for Asian and Black females, and just slightly for White females. In 2022, life expectancy increased for all groups, most significantly among Latino males and females.
  
  Black males have had a substantially lower, and Asian females a substantially higher, life expectancy than all other groups in all years.

1.3a All-Cause Mortality Trend (2000-2022) by Race and Ethnicity and by Sex, and All-Cause Mortality County Rankings (2022)

• The line charts show the trends in age-adjusted rate for mortality due to all causes, by race and ethnicity and by sex for the past 23 years. In a simple sense, all-cause mortality is the opposite of life expectancy—when all-cause mortality goes down, life expectancy goes up, and vice versa.
  
  The bar chart displays county rates of age-adjusted mortality (and 95% confidence intervals) due to all causes. The counties listed have the highest rates, and a reference line is included for comparison with the state rate.

• The “Trends by Race and Ethnicity” chart demonstrates that all-cause mortality rates decreased substantially (between 18 and 25 percent) among all race and ethnicity groups in California from 2000 to 2019.
  
  Starting around 2013-2015, mortality rates started to level off and even increase for all groups except for White individuals.
  
  Rates then increased sharply for all groups in 2020, although much less so for White individuals. In 2021, rates continued to increase sharply for Native Hawaiian/Pacific Islander (NHPI) and American Indian and Alaska Native (AIAN) populations, and increased slightly for Latino, White, and Asian populations. In 2021, rates decreased slightly for Black individuals from the peak in 2020.
  
  In 2022, rates decreased among all racial and ethnic groups. NHPI, AIAN, and Latino populations had the largest decreases.
  
  The very sharp and unprecedented increase among Latino individuals resulted in their all-cause death rate being higher in 2020 than any point in the past 20 years and, for the first time, higher than the rate among White individuals. The Latino population rate remained higher than the White rate in 2021, and then decreased to be lower than the White rate in 2022.
  
  The “Trend by Sex” chart shows that all-cause mortality rates decreased for both males and females over the 20-year period, until 2020. Males consistently had a higher all-cause mortality rate than females.

1.3b All-Cause Mortality Map - California, Bay Area, and Los Angeles County, 2018-2022

• These maps display all-cause mortality rates by location: statewide and regional views for the Bay Area and Los Angeles county (since these two regions include large urban cores with dense populations). The rates are compared at the subcounty level using Medical Service Study Areas (MSSAs), an aggregation of census tracts, to demonstrate variation between communities within each county.

• There are concentrations of high all-cause mortality rates within all counties and regions.
  
  There are some concentrations of higher rates in the central part of the State.

2.1 Multiple Lenses - Top 5 Conditions based on Multiple Measures

• This multi-chart emphasizes that there are many ways to view the health status of Californians. Public health looks across multiple measures to identify public health challenges.
  
  The first four charts use measures relating to deaths (number, years of life lost (YLL), increase, and race and ethnicity disparity). The next four charts look at additional lenses of public health burden (hospitalizations, emergency department (ED) visits, reportable diseases, and disability). Definitions of these measures can be found in the technical notes section below. Local Health Jurisdiction-level versions of this same multi-chart and a downloadable document can be found here. Additional complete data is available for LHJs upon request.

• Many conditions appear on more than one of these ranking measures, even though the measures assess very different levels of burden or impact:
  
  In 2022, ischemic heart disease was the top cause for total number of deaths and a leading cause for YLL (3rd). While much ameliorated compared to 2021, COVID-19 remained the fourth leading cause for both the total number of deaths and ED visits.
  
  Drug overdose saw by far the largest increase in death rates from 2012 to 2022. It was also the highest in YLL.
  
  Alcohol-related conditions are a leading cause for YLL (4th), increase in death rates (4th), and racial and ethnic disparity (4th).
  
  Mental health conditions are a leading cause for numbers of hospitalizations (3rd) and YLDs (2nd).
  
  Additional details on key findings for these measures are provided in later sections.

• COVID-19 is excluded as a cause in comparisons that involve years before the COVID-19 pandemic.

• *Conditions with fewer than 100 deaths in either period are excluded. Such conditions with large percent increases include:
  Influenza: 469.5% increase in age-adjusted death rate from 2012 (71 deaths) to 2022 (515 deaths)
  Respiratory failure: 142.2% increase in age-adjusted death rate from 2012 (71 deaths) to 2022 (228 deaths)

• **The most recent year of data for STDs is 2020, for TB 2021, for vaccine preventable diseases 2020, and for other reportable infectious diseases 2021.

• ***2019 is the most recent year these data from the Institute for Health Metrics and Evaluation are available.

2.2 Broad Condition Groups (6) - Rankings of Number of Deaths and Years of Life Lost in 2022

• This set of charts compares all causes of death using six broad condition groupings. These broad groupings are important for a very high-level understanding of the burden of death and disease, and these groupings (indicated by color) are used to frame the data in many of the charts that follow.
  
  The top chart ranks the number of deaths in California in 2022 according to the six broad condition groupings. The bottom chart shows the ranking of YLL according to the six broad condition groupings. YLL weights conditions that impact younger people and is sometimes referred to as “premature death”.

• Cardiovascular diseases caused the most deaths in 2022, followed closely by Other Chronic diseases. The Cardiovascular diseases broad condition group includes ischemic heart disease, stroke, hypertensive heart disease, and others. The Other Chronic disease broad condition grouping includes Alzheimer’s disease, Chronic Obstructive Pulmonary Disease (COPD), kidney disease, and others.
  
  Injuries caused by far the most years of life lost in 2022. This broad condition group includes drug overdose, alcohol-related conditions (including alcohol-related cirrhosis), suicide, homicide, falls, and road injury.

2.3 Public Health Condition Groupings - Top 15 Number of Deaths in 2022

• These charts show a more detailed view of causes, disaggregated into what we call the Public Health Level groupings. This grouping is based on programmatic areas of public health and/or clinical aspects of the conditions to facilitate public health planning and action.
  
  This chart shows the ranking of the top 15 causes based on numbers of deaths.

• At this Public Health Level, the conditions contributing the most deaths are ischemic heart disease, Alzheimer’s disease, and stroke. Note that three of the top five leading causes of death are in the Cardiovascular broad group.
  
  COVID-19 is the fourth leading cause of death, and the only cause in the Communicable disease broad condition group which is ranked in the top 15 causes based on number of deaths.

2.4 Public Health Condition Groupings - Top 15 Years of Life Lost in 2022

• This chart shows the ranking of the top 15 Public Health Level causes for years of life lost.

• The leading contributors to years of life lost are drug overdose, road injury, and ischemic heart disease. Note that five of the top seven leading causes of years of life lost are in the Injury broad grouping.
  
  In 2019, drug overdose deaths overtook ischemic heart disease as the top cause of years of life lost. This was the first time any cause ranked higher than ischemic heart disease for at least two decades. In 2020, drug overdose continued to rank higher than ischemic heart disease, but in 2021 COVID-19 was the top cause (1st). Drug overdose became the top cause of years of life lost again in 2022. Beginning in 2022 road injury also ranked higher (2nd) than Ischemic heart disease. Due to the magnitude of deaths from ischemic heart disease, it has been a leading cause both in terms of number of deaths and years of life lost for the past 20 years.

2.5 Public Health Condition Groupings - Top 15 based on Percent Increases in Age-Adjusted Death Rates in Different Periods

• This multi-chart shows the ranking of the top 15 Public Health Level causes based on percent increase in rates across several periods. The first two charts present increases in the “pre-pandemic period” for the greatest ten year increases from 2009 to 2019, and the greatest five year increases from 2014 to 2019. The next set of charts presents increases during the COVID-19 pandemic period beginning with the two year increases from 2019 to 2021 and then the most recent single year increases from 2021 to 2022. A detailed data table with these increases is included in Appendix A.1.

• Deaths from drug overdoses increased more than any other condition both from 2009 to 2019 and 2014 to 2019; and continued to increase sharply from 2019 to 2021, second only to COVID-19. The increase in drug overdose deaths slowed significantly in 2022. It was not among the top 15 causes with the largest increases from 2021 to 2022.
  
  Other than COVID-19 and drug overdoses, conditions that increased substantially in the pandemic period include obesity, homicide, alcohol-related, road injury, and poisonings (non-drug related). The very large increase in homicides between 2019 and 2021 is striking—except for COVID-19, drug overdoses, kidney disease, Parkinson’s disease, and obesity, this is the largest increase seen compared to any other conditions in any of these periods.
  
  These recent increases are concerning and need further exploration, including their relationships to the pandemic. More detail and information related to increases in the pandemic period can be seen in the CDPH Excess Mortality Data Brief. Of note, several of these conditions that have increased are in the “deaths of despair” category. The term “deaths of despair” was introduced by Case and Deaton in 2015 (Case & Deaton, 2015), and has generated substantial attention as an area of increasing deaths needing focused public health attention. Per Case and Deaton, “deaths of despair” include drug overdoses, suicides, and deaths due to alcoholic liver disease. Several behavioral health related conditions in this category may be influenced by interrelated drivers including stress and substance use. In their original work, the authors noted higher rates among younger, less educated White populations. In California, the deaths of despair due to drug overdoses are very high and increasing among younger and middle-aged AIAN, Black, White, and NHPI populations.
  
  Other conditions that increased substantially in the pre-pandemic periods include kidney disease, Parkinson’s disease, congestive heart failure, other neurological conditions, hypertensive heart disease, Alzheimer’s disease and road injury.
  
  The extremely large increase in kidney disease in the pre-pandemic period is striking. The specific reasons for this increase are not clear but are being investigated and warrant further investigation.

• Note: Conditions with fewer than 100 deaths in all time periods are excluded.

2.6 Public Health Condition Groupings - Top 15 based on 10-year Percent Decreases in Age-Adjusted Death Rates, 2012 to 2022

• This chart shows the ranking of the top 15 Public Health Level causes based on percent decrease in rates from 2012 to 2022.

• Deaths from hepatitis decreased more than 60% over this 10-year time period. This decrease is likely due in large part to the tremendous advances in treating hepatitis C, and to a range of public health efforts.
  
  Decreases from other conditions, like lung cancer, are also likely due to well-documented public health efforts. Many other decreases warrant further investigation.

*Conditions with fewer than 100 deaths in either period are excluded. Such conditions with large percent decreases include:
Meningitis: 46.76% decrease in age-adjusted death rate from 2012 (86 deaths) to 2022 (50 deaths)
ARDS: 30.01% decrease in age-adjusted death rate from 2012 (65 deaths) to 2022 (57 deaths)

3.1 Trends in Broad Condition Groups - Age-Adjusted Death Rate, 2000-2022

• This chart shows the age-adjusted death rate trends of the six broad condition groupings in California from 2000-2022.

• Great progress has been made in the past 20 years, through 2019, with decreasing death rates for Cardiovascular disease, Cancer, and Communicable disease. In contrast, death rates for Other Chronic diseases and Injury increased somewhat over this period. The increase in Other Chronic diseases is due in large part to increases in deaths from Alzheimer’s disease.
  
  In 2020 and 2021, deaths increased very sharply for Communicable diseases due to COVID-19; and increased for Injury in both years. Deaths due to Communicable diseases decreased sharply in 2022, while Injury deaths decreased slightly. Deaths from Cancer continued the encouraging downward trend of the prior 20 years through 2022.

3.2 Trends In Top Public Health Conditions (by Broad Condition Groups), 2000-2022

• These charts provide a deeper look into the trends in cause of death by showing the age-adjusted death rate trends of the top 5 Public Health conditions within each broad condition group.

• The previous chart showed good progress within the Cardiovascular and Cancer broad condition groups during the past couple of decades. These more detailed charts reveal the main drivers for those two downward trends, which are ischemic heart disease and lung cancer. (Ischemic heart disease decreased every year from 2000 to 2022, except for the slight increase in 2020. The reason for this reversal in trend in 2020 is not known and warrants investigation.) Furthermore, pneumonia deaths from the Communicable group also greatly declined since 2000. In contrast, Alzheimer’s disease in the Other Chronic group and drug overdoses in the Injury group have sharply increased since 2000.
  
  The increase in Alzheimer’s disease appears to be driving the observed increase in the broader Other Chronic group. In contrast, chronic obstructive pulmonary disease (COPD) deaths have decreased substantially (but remain a leading cause of death).
  
  In the Injury broad condition group, drug overdoses have more than quadrupled, and road injury is increasing after a previous decline. Alcohol-related deaths increased sharply in 2020 and again in 2021, but decreased in 2022. Homicides, after many years of decreasing or level rates, increased sharply in 2020, and increased again in 2021, but decreased in 2022.
  
  In the Perinatal condition group, deaths due to neonatal conditions declined significantly since 2000, and reached their lowest level in 2020, but increased in both 2021 and 2022.
  
  Note: The y-axis scales for each chart are different.

3.3 Trends in Age-Adjusted Rates for Top 15 Public Health Level Conditions (log-y-axis), 2000-2022

• This chart offers a different perspective than the previous charts by looking at trends over the past two decades for public health-level conditions overall, regardless of the broader groups. These are the conditions with the top 15 age-adjusted death rates in 2022.

• Age-adjusted death rates from ischemic heart disease, stroke, lung cancer and COPD decreased greatly over the past 20 years.
  
  This very encouraging decrease in ischemic heart disease is likely due to increasing use of statins for treatment of high cholesterol, and due to prevention behaviors related to healthier nutrition and exercise. The substantial decrease in lung cancer is very likely due to decades-long widespread State and National tobacco control efforts. In contrast, Alzheimer’s disease has more than doubled since 2000, resulting in it having the second-highest rate from 2008 onward (except third highest in 2020 and 2021 because of COVID-19).
  
  Also of note are drug overdoses and kidney diseases. While these conditions are lower on the list, their age-adjusted death rates have increased dramatically since 2000.

• A logarithmic scale is used for the y-axis in order to be able to clearly see the trends for all these conditions on one chart. Also, on a logarithmic scale, lines are parallel if the relative changes (i.e., percent change) over a time period are the same.

4.1 2023 Preliminary Data: Top 5 Causes of Death and Years of Life Lost

• This chart shows leading causes of death and the leading causes of Years of Life Lost (YLL) in 2023.

• Ischemic heart disease was the leading cause of death (1st) and a leading cause of YLL (2nd) in 2023; Alzheimer’s disease was a leading cause of death (2nd). Drug overdoses were the leading cause of YLL (1st) in 2023. Stroke was a leading cause of death (3rd) and road injury was a leading cause of YLL (3rd).
  
  

5.1 Race and Ethnicity Age-Specific All-Cause Death Rate Ratio with White Population as Reference Group, 2020-2022

• This chart shows the ratio of age-specific AIAN, Asian, Black, Latino, and NHPI population rates to the corresponding age-specific White population rates (White individuals are used as the reference group since they have historically been the one of the largest groups in the State, and are, on average, relatively advantaged).
  
  A rate ratio of 1.0 means that the rates are the same for both groups.
  
  Appendix Table A.2 shows the numbers of deaths and rates that are the basis for the rate ratios in the chart.

• Of the many observations that can be seen in this chart, one especially important observation is seen in the “Black:White” rate ratio column. In the 0-4 year old age group, the death rate is over 3 times higher for Black infants/toddlers than for White infants/toddlers. For children/teens/early 20’s and 35-44 age group, the rates are over 2 times higher for Black populations than White populations. In general, this disparity is greater at younger ages and the ratio decreases as age increases. Among the oldest age group, the rate among Black individuals is slightly less than the rate among White individuals. This difference likely reflects the outcome of disparities in death rates earlier in the life course (with more deaths among the Black population at younger ages), leaving only a smaller number of relatively healthy Black people in the oldest age group.
  
  Many complex factors interweave to create these disparities and patterns. The much higher rates of death among the Black population across most age groups are due in large part to the cascade of social determinants of health (e.g., discrimination/racism, poverty/wealth) and historical and structural inequities (e.g., housing, education, employment) that impact health and life expectancy.
  
  Among the Latino population, rates are better (lower) than, or very similar to, White individuals ages 25 and older, but worse (higher) between ages 0 and 24, with the greatest difference at the youngest (0-4) age level.
  
  Among AIAN and NHPI individuals, the patterns are similar to the pattern described for Black individuals, and important for the same reasons. Because of the much smaller population sizes of these two groups, there is more variability in the numbers.
  
  Among Asian individuals, the rates of death are lower than the rates among White individuals. However, the overall low rates likely mask differences between different Asian subgroups, as noted in Section 9.3 below.

• *Data are suppressed per the California Health and Human Services Agency Data De-Identification Guidelines

• The black line at the end of each bar is the 95% confidence interval for the rate ratio, calculated with the rate ratio function of the epitools package in R.

5.2 Change in Race and Ethnicity All-Cause Mortality Rate Disparity, 2000-2022

• This chart presents information on trends in all-cause mortality by race using rate ratios.

• This chart shows changes over time in the rate ratio of the other race and ethnic groups compared to White populations. It shows increasing differences from the White population rate for all groups starting in the early to mid-2010s, with a sharp acceleration in these disparities in 2020 due to the impact of COVID-19. In 2021, this sharp acceleration continued for NHPI and AIAN populations, leveled off for Latino populations, and decreased slightly for Black and Asian populations. The disparities between other race and ethnic groups and the White population decreased in 2022, especially among NHPI, AIAN, and Latino populations. (The chart in section 1.3a serves as important background for this chart.)

5.3 Ranking of Race and Ethnic Disparities in Death Rate, 2020-2022

• This chart ranks causes of death by racial and ethnic disparities. Disparities are measured using rate ratios, comparing the rate among the race and ethnic group with the highest rate to the rate among the race and ethnic group with the lowest rate for each cause of death. Data for 2020-2022 are combined for statistical stability.
  
  A rate ratio near one means there is little difference between the groups with the highest and lowest rates. The bar size shows the rate ratio; the labels inside the bar show the group with the highest rate and the lowest rate (highest:lowest) for that cause.
  
  Appendix Table A.3 shows the numbers of deaths and rates that are the basis for the rate ratios in the chart.

• The top disparity in death rates is for obesity (1st), with the NH/PI population rate almost 22 times the rate among the group with the lowest rate (Asian population).
  
  Homicide has the second highest disparity (2nd), with the Black population rate more than 16 times the rate among the Asian population.
  
  Another leading disparity is for HIV/STD (3rd), where the Black population rate is about 13 times higher than the Asian population rate.
  
  The next leading disparity, alcohol-related conditions (4th; 13 times), and another leading disparity, drug overdoses (6th; 10 times), both have the highest rates among AIAN individuals and the lowest rate among Asian individuals.
  
  An additional leading disparity is for tuberculosis (5th), with the Asian population rate more than 11 times higher than the rate among White individuals. (The high rate among Asian individuals in California is known to be associated with persons born outside of the United States. Report on Tuberculosis in California, 2019).

5.4a Top Ranking Causes by Crude Death Rate, 2020-2022

• The charts shown in sections 5.4a, 5.4b and 5.4c look at deaths, hospitalizations, and ED visit data by race and ethnicity; showing all race groups, with the ranks sorted based on one selected race group.
  
  The same charts, for all age groups and all California counties are also available in the California Community Burden of Disease Engine (CCB) in the “Ranks” section, in the “AGE RACE FOCUS” Tab.
  
  Additionally, information about disaggregated race and ethnicity groups is available in section 9.3.

• This chart is for deaths, ordered based on rates among AIAN individuals, and indicates that the leading causes of deaths among AIAN individuals include drug overdoses (3rd) and alcohol-related conditions (4th). These two causes of death do not rank among the top five causes of death for any other race and ethnic group (except drug overdose deaths are 5th for Latinos).
  
  

5.4b Top Ranking Causes by Crude Hospitalization Rate, 2020-2022

• This chart is for Hospitalizations, ordered based on rates among Black individuals, and indicates that the leading causes of hospitalization for Black individuals are septicemia (1st) and mental health related causes (3rd and 5th).
  
  The chart indicates that this is not the same ordering for all other race and ethnic groups. For example, among both Asian and Latino populations, “other complications of birth” is the second leading cause of hospitalization, which is only the ninth leading cause among Black populations.

5.4c Top Ranking Causes by Crude Emergency Department Rate, 2020-2022

• This chart is for Emergency Visits ordered based on rates among Black individuals, and indicates that for all race and ethnic groups, abdominal pain, chest pain, and upper respiratory infections are leading causes for ED visits.
  
  The chart also shows that the rates of ED visits for many conditions are higher among Black persons than other groups. These differences are likely due to many factors, including reduced access to health care services leading to increased use of ED for primary care among Black populations, and a cascade of many other factors, leading to a higher incidence of many of these conditions.

5.5 Trends in Mortality Rates and Percent Change in Mortality by Age Group from 2010 to 2022

• Chart (i) shows age-specific trends in mortality rates by age group from 2010 to 2022. Chart (ii) shows percent changes in mortality rates by age group from 2010 to 2019 (pre-pandemic period), 2019 to 2021 (pandemic period), and 2021 to 2022 (recovery period).

• 25-34 age group, where rates increased steadily and strongly; and for the 35-44 age group, where rates increased somewhat.
  
  From 2019 to 2020, due to COVID-19, rates increased for all groups (except infants/toddlers). From 2020 to 2021, rates continued to increase sharply for the 25-34 and 35-44 year old age groups. For some other age groups, the rates also continued to increase, and for others they leveled off or decreased. From 2021 to 2022, rates decreased among all but the two youngest age groups, 0-4 and 5-14.
  
  Over the pre-pandemic period from 2010 to 2019, the rate increased 52% among the 25-34 year age group, 14% among the 35-44 year age group, and decreased among all other groups.
  
  The percent change in mortality rate by age group during the pandemic period (2019 to 2021) increased for all age groups (except for infants/toddlers); with the largest increases being among younger adults (25-34 and 35-44) at 50% and 61% respectively.
  
  The percent change in mortality rate by age group during the recovery period (2021 to 2022) decreased for all age groups except for infants/toddlers and 5-14 year age group; with the largest decreases being among the 45-54 and 55-64 age groups at 17% and 16%.

5.6 Leading Causes of Death Across the Life Course, 2020-2022

• This chart shows the five leading causes of deaths across the “life course” for each age group. The chart shows the rank, the number of deaths, and is color coded for the broad condition group for each cause of death. This same chart, with additional stratification by sex and race and ethnicity, is available for all local health jurisdictions here.

• As expected, the number of deaths are much larger among the older age groups than the younger groups.
  
  The youngest age group 0-4 is most impacted by neonatal conditions and congenital anomalies.
  
  From 15-24 to 35-44, the leading causes of death are mostly injury-related, such as deaths due to drug overdoses, road injuries (also the leading cause among 5-14), suicide/self-harm, etc. Drug overdose is the leading cause of death between the ages of 15 and 44.
  
  Ischemic heart disease starts to appear as a leading cause in the 45-54 age group and becomes the leading cause of death among Californians between the ages of 65 to 84.
  
  Breast cancer (among females) appears as one of the leading causes in the 45-54 and 55-64 age groups.
  
  Lung cancer appears as one of the leading causes of death between the ages of 65 to 74.
  
  The top cause of death among the oldest Californians (85+) is Alzheimer’s disease.
  
  
  
  In general, this “life course” chart shows a progression from multiple causes in the youngest age groups, to Injury causes in middle age groups, to Cardiovascular, Cancer, and Other Chronic diseases in older age groups; in addition to COVID-19 in middle and older age groups in the pandemic period.

5.7a Top Ranking Causes of Deaths, Hospitalization, and ED Visits, Age 15-24, 2020-2022

• The charts in sections in 5.7a, 5.7b, and 5.7c show the leading causes of deaths, hospitalizations, and ED visits for a selected age group at different stages of the life course (starting with the 15-24 age group) using data from 2020 to 2022 combined.
  
  These age groups have been selected to highlight different patterns in causes of deaths, hospitalizations, and ED visits at each stage.
  
  Additional age groups, race and ethnicity, and county level views for these same ranked data can be seen in the California Community Burden of Disease Engine (CCB) in the “Ranks” section, in the “DEATH HOSP ED” Tab.

• This first chart is for the 15-24 years age group, and shows that five of the top six leading causes of death, and many of the top causes of ED visits, are injury-related. The top causes of hospitalization are mental health and perinatal-related. Drug overdoses, road injury, homicide, and suicide are by far the leading causes of death in this age group.

5.7b Top Ranking Causes of Deaths, Hospitalization, and ED Visits, Age 45-54 , 2020-2022

• This next chart is for the 45-54 years age group, and shows that 1) the leading causes of death include COVID-19, injury (in particular drug overdoses and alcohol-related), and cardiovascular; 2) the leading cause of hospitalization in this group (and in many of the older age-groups) is septicemia, followed by COVID-19 and schizophrenia; and 3) ED visits are due to a wide range of conditions.

5.7c Top Ranking Causes of Deaths, Hospitalization, and ED Visits, Age 85+, 2020-2022

• This third chart is for the 85+ years age group and indicates that between 2020 and 2022, Alzheimer’s disease is the leading cause of death followed by Cardiovascular diseases (four of the next five leading causes), and COVID-19.
  
  Septicemia is the leading cause of hospitalization; other leading causes include Cardiovascular diseases and fractures.
  
  Urinary tract infections are a leading cause of ED visits (2nd); three of the five leading causes, including the top cause, are Injuries.

6.1 Conditions associated with Years Lived with Disability

The YLD measure accounts for the number of years lived with an illness or health condition and the severity of the condition throughout life.

These charts show: a) the top 10 causes associated with the greatest number of YLDs in 2009 and in 2019; and b) the top causes by selected age groups in 2019.

6.2 Risks Associated with Disability Adjusted Life Years

Disability-Adjusted Life Years (DALYs) are defined as the sum of years of life lost (YLLs) due to premature mortality and the years lived with disability (YLDs). DALYs are one important way to assess the degree of health burden associated with health risks.

These charts show the top 10 risk factors associated with the greatest magnitude of DALYs: a) in 2009 and in 2019; and b) by selected age groups in 2019.

7.1 Ischemic heart disease

7.2 Alzheimer’s disease

7.3 Drug overdose

7.4 Road injury

7.5 Obesity

7.6 Homicide

8.1 Life Expectancy (Mean) by Quartiles of Selected SDOHs, 2018-2022

• These charts show the mean (or average) community life expectancy based on quartiles of each social determinant.

• Lower life expectancy is associated with lower rates of voting, education, housing affordability, extra income, health insurance coverage, and internet access.

• The y-axis does not start at 0, but rather at age 70, so that the important differences in life expectancy can be seen clearly.

8.2 Communities with Highest and Lowest Life Expectancy, 2018-2022

• This table shows the communities (MSSAs) with the 10 highest and lowest levels of life expectancy in the State. It also presents the mortality rate, percent did not vote, percent with educational attainment of high school graduation and below, percent with no extra income, percent with no internet access, percent without health insurance, and percent with housing burden, as well as overall population.

• This tabular view of the data highlights the strong community-level associations seen above and emphasizes some extreme differences in life expectancy. Life expectancy in the least advantaged “Clearlake /Clearlake Oaks” community in Lake County at 70.0 is over 17 years less than the life expectancy of 87.4 in the most advantaged community of “Bel Air /Beverly Glen /Beverly Hills /etc.” in Los Angeles County.

8.3 Mean Age-Adjusted Cause-Specific Death Rate by Quartiles of Selected SDOHs, 2018-2022

• These charts explore the relationships between social determinants of health (SDOH) and specific causes of death at the community (MSSA) level. Like the figure in section 8.1, each SDOH is divided into quartiles, with quartile 1 being the most advantaged and quartile 4 being the least advantaged. The four causes of death selected are: COVID-19, ischemic heart disease, suicide, and drug overdose.

• Strong relationships are seen in COVID-19 and ischemic heart disease with almost all six of the SDOHs. In contrast, the patterns seen in suicide and drug overdose are less clear, as the least advantaged communities do not always have the highest death rate, and the most advantaged communities do not always have the lowest death rate.

8.4 County Level Social Determinants, Life Expectancy, and Death Rate for Selected Causes of Death, 2018-2022

• These maps show that, at the county level, SDOH (not voting, education, no extra income, no health insurance, no internet access, housing burden), life expectancy, and death rates for selected causes are roughly correlated. In general, regions with greater disadvantage for each social determinant experienced relatively lower life expectancy and higher death rates.
  
  With exceptions, this pattern is seen particularly in the northern and Central Valley portions of the State, both of which include more rural areas than other regions in the State.
  
  Regions with the highest death rates for drug overdose and suicide are concentrated in the north with elevated rates throughout several additional rural areas, while the hot spots for COVID-19 and ischemic heart disease are more widely spread across the state.

• *Data are suppressed per the California Health and Human Services Agency Data De-Identification Guidelines

• For all maps, the color shading goes from lighter for more advantaged or better health outcomes, to darker for less advantaged or worse health outcomes.

9.1 Mental Health

• This exploratory section examines mental health conditions. These conditions affect more than half of people in the United States over the course of their lifetime, one in five people every year, and are contributing factors to worse overall health.
  
  Here, we have conducted analyses of emergency department visit and hospitalization rates for the broad mental health condition categories of: 1) anxiety and related disorders (including trauma and stressor-related disorders such as post-traumatic stress disorder), 2) mood disorders, 3) schizophrenia and related disorders, and 4) all other mental health conditions not fitting into one of these three other categories. These data were then grouped by race and ethnicity, and further by age for mood disorders and for schizophrenia, to examine if disparities in rates of ED visits and hospitalizations exist and for which age groups.
  
  
  
  Compared with overall prevalence of mental health conditions, and the number of ED visits and hospitalizations, the number of deaths due specifically and directly to mental health is quite low. As currently grouped, there were 243 deaths from mental health-related conditions in 2022, but we do not include information about those data in this initial and exploratory section because of the small numbers and because we need further assessment and clinical input regarding the proper and optimal use of these codes.

• Taken together, these preliminary and exploratory data demonstrate a significant disparity in mental health conditions with Black populations having much higher rates of ED visits and hospitalizations for such conditions than other racial and ethnic groups.
  
  Further, this disparity for Black populations is seen strikingly for almost all age groups, in particular among young people, as shown for ED visits for mood disorders and schizophrenia.
  
  Understanding and addressing the issues underlying these disparities is both important and challenging. Some of the multiple and complex issues include inequities in access to mental health care and prevention services; the impact of bias and racism in the “labeling” and diagnosis and treatment of mental health problems by law enforcement, courts, educational systems, health care, and mental health professionals; as well as the potential impact of interrelated risk factors and social determinants of health (neighborhood disadvantage, community and family trauma, economic inequality) in the actual production of these mental health conditions through intensive exposures to trauma and toxic stress.

9.2 Rural Health in California

• This exploratory section examines how an important dimension of the places in which people live, rural/urban categories, may impact their health. This section should be considered preliminary.
  
  Rural/urban categories are an important concept related to health. Nationally, data demonstrate that rural populations experience comparatively worse health outcomes than the rest of the population overall. Rural risk factors include geographic isolation, lower socioeconomic status, higher rates of health risk behaviors, limited access to care, and many others (see Rural Health Disparities Overview - Rural Health Information Hub).
  
  Rural/urban categories are defined in different ways by different systems. One system used by the Federal Health Resources and Services Administration (HRSA) are Rural-Urban Commuting Area (RUCA) codes based on the same concepts used by the Federal Office of Management and Budget (OMB) to define county-level urban and rural areas, but at the census tract level. These codes are on a 21-level continuum to account for varying levels of rural/urban categories across the full continuum (see USDA ERS - Rural-Urban Commuting Area Codes). We have collapsed these codes into seven category classifications for all census tracts in California as follows:
  
  
  • Urban Core, Low Commuting - Urban 1.0: Metropolitan
  • Urban Core, High Commuting - Urban 1.1: Metropolitan
  • Urban Area, High Commuting - Urban 2.0: Metropolitan
  • Urban Area, Low Commuting - Urban 3.0: Metropolitan
  • Large Rural Area - Large Rural: Micropolitan
  • Small Rural Area - Small Rural
  • Isolated Rural Area - Isolated Rural

9.3 Detailed Race and Ethnicity

• This section focuses on mortality using disaggregation of broad race and ethnicity into detailed groups. This type of work is important since detailed race and ethnicity “sub-groups” are likely to be heterogeneous with respect to many characteristics including health outcomes, health care access and health-related behaviors, and upstream social determinants of health. Analyses based on these more specific “sub-groups” can inform different strategies in terms of public health programs and interventions.
  
  The analyses of these detailed (or “disaggregated”) race and ethnicity data required procedures, some assumptions, and use of population data sources not used elsewhere in this document. These analyses should be considered preliminary and interpreted with caution. However, because of the imperative for assessing and evaluating these data, they are shared here.
  
  Detailed issues and limitations associated with CDPH disaggregated race and ethnicity data in general can be found in the recently posted page: Asian and Pacific Islander Data Disaggregation and the associated Asian and Pacific Islander Data Disaggregation Highlights - California Assembly Bill 1726 (2016) - July 2022. Considerations, limitations, and issues with the specific data below can be found starting on page 34 of that document.

9.4 Comparison of Two Methods for Tabulation of Grouped Race and Ethnicity Data

• This section explores differences in numbers of deaths, population sizes, and corresponding rates using two different approaches to tabulating race and ethnicity data for 2022. These two approaches relate directly to implementation of California Assembly Bill 532 (2015)

• The two approaches to tabulating race and ethnicity explored here are:
1. The “common” approach often used at CDPH and elsewhere where race and ethnicity tabulation is based on a mutually exclusive and exhaustive sequential grouping where persons are classified first as whether they are “Latino/Hispanic” if their ethnicity is Latino/Hispanic, regardless of any information on race Next, if not “Latino/Hispanic,” and identifying as more than one race (except not counting “Other”) they are classified as “Multirace”; then finally if not “Multirace,” they are classified as a single race (e.g., Non-Hispanic White, Non-Hispanic Black, etc.). This is referred to here as the mutually exclusive and exhaustive approach (MEE).
2. Race is based on a race being noted alone, in combination with any other race, or in combination with Latino/Hispanic ethnicity. With this approach the groupings are not mutually exclusive—meaning persons identifying with more than one race and/or ethnicity would be included multiple times in such tables. This is referred to here as the Any approach.

Table – Numbers of deaths, population size, and crude death rates comparing MEE to Any approaches, and associated percent change between the two approaches.

• In the table, all death numbers and population numbers are greater when using the Any approach compared to the MEE approach. This increase in numbers is intrinsic to the two approaches. For all data, with the Any approach death or case counts and population numbers will always be larger (or possibly the same) than the corresponding MEE approach counts and population numbers.
• In the table, all calculated rates decrease when using the Any approach compared to the MEE approach. This occurs because in all instances the MEE to Any percent increase in the population numbers is greater than the MEE to Any percent increase in deaths numbers. This pattern is not intrinsic to the two approaches and may differ with other data sources.
• Of particular note, there are large increases in AIAN deaths (162%) and very large increases in AIAN population (803%) between the MEE and Any approaches, respectively. Again, since the increase in the population is much larger than the increase in the number of deaths, the calculated Any rate decreases substantially compared to the MEE rate.

Summary

• This exploration of two approaches to tabulating race and ethnicity yields potentially important observations but is complex and preliminary. The Any approach likely better describes the full burden of death across all conditions and for all race and ethnic groups, particularly for the American Indian/Alaska Native group, from the perspective of death or case counts. However, in many situations the Any approach also leads to lower calculated burdens in terms of rates and can lead to smaller calculated racial and ethnic disparities when using standard measures like rate ratios.
• The approaches used here require further discussion with internal CDPH partners, local health departments, and groups using and advocating for more race and ethnicity data, among others.

Notes and Methods

• The death data are based on all causes and are from the California Integrated Vital Records (CalIVRS) system, as described in the Technical Notes section.
• For meaningful comparability of Any versus MEE approaches, the population denominator data are from the 2020 Decennial Census Tables P1 (for Any) and P2 (for MEE). Elsewhere in this Core Module, including for MEE charts and tables, denominator data are from the California Department of Finance (DOF), but denominator data for Any are not currently available from DOF.
• At present, all rates in this section are “crude rate”" rather than “age-adjusted rates.” The rates in this section are only for this exploration of the Any versus MEE approaches and should not be used for any other purposes.
• For various reasons it is not meaningful to show data for the Any approach for the following race group designations: Other, Multirace, Unknown, Latino/Hispanic, or Total. Detailed information regarding why this is the case this will be added to subsequent versions of this exploration.

9.5 Multiple Cause of Death Analysis

• All cause-specific death data shown so far in the Core Module, and in most presentations of death data, are based on the single “primary” (or “underlying”) cause of death. In this section of the Core Module we also explore deaths based on any listed “secondary” (or “contributory”) causes of death. In the California death data system, up to 19 secondary causes of death can be listed in addition to the required primary cause of death. While it is rare for a full 19 secondary causes to be listed, in most cases some secondary causes are listed (e.g., for 2021 death certificates, 71% list 1 to 4 secondary causes, and 16% list 5 or more). Secondary causes are also sometimes referred to as “multiple causes of death (MCOD)”.
  
  Note: For some analyses of death data, use of MCOD information is essential, including, for example: in relation to drug overdose deaths (to identify specific substances), in relation to child maltreatment deaths, and mental health-associated deaths.

A - Tables

Data Sources 

A majority of the charts and tables in this module are based on death data: 

• The death data used are from the California Integrated Vital Records (CalIVRS) system, based on death certificates/reports transmitted to the California Department of Public Health, Center for Health Statistics and Informatics (CHSI).  Details of the exact data sets used, aggregation of International Classification of Disease 10th Revision (ICD-10) codes into causes of death, calculation methods, demographic and geographic detail, data de-identification, and a wide range of other particulars are available in the Technical Documentation section of the California Community Burden of Disease Engine (CCB-Tech). 

  • All sections in this Core Module use the single underlying cause of death ICD-10 code, except for the Multiple Cause of Death Analysis section.

  • All measures using vital statistics death data are limited based on the accuracy of the coding of cause of death on the death certificate 

Other data used include: 

• Hospital inpatient discharges and Emergency Department encounters, from the California Department of Health Care Access and Information (HCAI). Details of the exact data are in the CCB-TECH. 

• Reportable infectious disease data, from the CDPH Center for Infectious Disease, obtained via the CHHS Open Data Portal. 

• Disability and risk data and charts from the Institute for Health Metrics and Evaluation (IHME),  downloaded from their website. 

• Social determinants of health data from the US Census American Community Survey. 

• And, a wide range of Let’s Get Healthy California Progress Indicators, from multiple sources. 

Measures 

Primary measures used with death data include number of deaths, crude death rate, age-adjusted death rate, and life expectancy. 

• Number of deaths (or hospitalizations, etc.) describes the absolute magnitude of deaths, and is a clear and easily understood measure. All other things being equal, the number of deaths will be larger in areas with larger populations. This measure does not take into account the “age distribution” or size of the population.  

• Crude Death Rate takes the size of the population into account by dividing the number of deaths by the number of people in the population (multiplied by 100,000 for interpretability, i.e. number of deaths per 100,000 people).  

• Age-adjusted Death Rate takes into account or “controls” for the age distribution of the population where the rate is being assessed. It is the rate that would have existed if the population had the same age distribution as a reference population. This allows for comparisons between populations with differences in age distributions, accounting for the fact that age itself is generally correlated with higher mortality. 

• Life Expectancy (specifically, “Life Expectancy at Birth”) is a familiar and widely used measure, which summarizes in one number the ‘force of mortality’ in a population, and provides a valuable single measure to compare the overall health status between populations. Its calculation is complex, but is generally interpreted as the number of years people born in a particular year are “likely” to live. 

In addition to these measures, a number of other measures are used, specifically in the “Multiple Lenses” section and other ranking charts. Explanations of these measures are:  

• Years of Life Lost (YLL) (sometimes referred to as “premature mortality” and sometimes as “years of premature life lost (YPLL)”) can be calculated using two different methods.
  
  The first method is simpler, and is based on summing for all deaths, the number of years prior to age 75 that each death occurs, with 0 YLL used for deaths occurring at ages >= 75. This method has the advantage of (1) emphasizing more strongly deaths that occur at younger ages and (2) being simpler to explain and understand.
  
  The second method is that of Global Burden of Disease Study and the Institute for Health Metrics. With this method the YLL for each death is based on the age at death and the additional number of years a person of that age living in an optimal setting could be expected to live. For example, someone dying at birth would be associated with 91.94 YLL, someone dying at 25 associated with 67.08 years, and someone dying at 98 with 3.70 years.  These additional number of years at each age are based on data from nations with longest lived populations, as presented in a table from the WHO GBD Study. In the Core Module the first method is used in all instances except where data are used directly from IHME; IHME uses the second method.

• Percent Increase measures the change in the death rate between two different years, and shows which conditions are increasing (or decreasing) most rapidly. This is measured by showing the percentage increase in the age-adjusted death rate. “Age-adjusted” death rates are used to account for the impact of the changing age distribution of the California population on the measure. Because this measure focuses on the degree of increase it may sometimes highlight a condition or group for which the absolute number of deaths is relatively small, but the percent increase is great. 

• Disparity Ratio  measures the difference in the death rate between racial/ethnic groups for the same condition using combined data from a three-year period. The measure compares the age-adjusted death rate in the group with the highest rate to the group with the lowest rate. A large ratio between the two rates indicates a large disparity. 

• Years Lived with Disability is based on calculations and modeling done by the Institute for Health Metrics and Evaluation. These models utilize assumptions and multiple data sources to produce reliable California-specific estimates of years lived with disability. (expressed here as rate per 100,000 population, most recent year available 

Data Time Frames  

• This 2024 Core Module generally includes data through the most recent year for which complete data are available, 2022. For some charts data for just 2022 are shown and for others, mainly the trend charts, data for 2000 through 2022 are shown. 

• In some cases, for statistical stability and/or data deidentification purposes, years are aggregated into 3- or 5-year groups. 

• Because of the importance of showing some high-level data for the most recent time period available, especially in this COVID-19 era, data for 2023 are included in the 4th section. These data are preliminary—final death data for a given year are not available until the fall of the following year. 

Additional Notes 

• The data and charts in the Core Module are primarily driven by The California Community Burden of Disease Engine (CCB).  The CCB is a dynamic system of morbidity, mortality, and social determinants of health data;  standard value sets and tools; and modular code,  using R. The CCB provides a detailed interactive visualization platform for discovery and deeper understanding of health outcomes for public health action; and resources to quickly identify and address emerging issues and questions, with rapid deployment of analyses, visualizations, and other data tools and resources, accessible for use by public health practitioners and partners. 

  • The death and hospitalization data in the Core Module use the CCB data processing, measure calculation, and data visualization machinery. Key aspects of the CCB that facilitate insights in the Core Module include the California Community Burden of Disease Condition List, a hierarchical list of about 70 causes of death, that allow for both broad and detailed views mortality burden; hierarchical views of place, including the state, county, community and census tract levels; over 20 years of data; and carefully constructed measures and formulas.  Details of these features are described in the CCB-Tech. 

• The “Medical Service Study Area (MSSA)” geographic unit is used in several places in the report to represent “community”.  MSSAs are aggregations of census tracts, and are constructed by the HCAI with each decennial census. MSSAs are a useful surrogate for “communities” because there are 542 MSSAs for the 2010 census, providing much more geographic granularity than the 58 California counties and much greater numerical/statistical stability than the 8000+ California 2010 census tracts. Further, they are aligned with “communities” in the important sense of geographic, cultural, and sociodemographic similarities (although this is generally more true for urban than rural MSSAs, because of the larger size of MSSAs in rural areas). 

• Grouping of ICD-10 cause of death codes into useful categories is described in detail in the CCB-Tech. Because of their visibility in this Core Module and because their construction may differ from that used in other reports of California death data, we note that:  

  • “Drug overdose” deaths include “accidental poisonings by drugs” codes, “substance use disorder codes” (but not “alcohol use disorder”), and “newborn (suspected to be) affected by maternal use of drugs of addiction” codes. This approach was determined based on discussion with the CDPH Substance and Addition Prevention Branch (SAPB) and on the CDC “Consensus Recommendations for National and State Poisoning Surveillance”. 

  • “Alcohol-related conditions” includes customary causes like “alcohol abuse” and “alcohol dependence disorder”, as well as conditions that may be grouped elsewhere in other systems, especially “Alcoholic liver disease”. This approach was determined based on discussion with the CDPH Injury and Violence Prevention Branch (IVPB) and on the CDC Alcohol-Related Disease Impact (ARDI) ICD-10 codes (using 100% Alcohol-attributable codes only).